eDiscovery Agents
A review team that reads a million documents without fatigue — finding the responsive, flagging the privileged.
eDiscovery agents are AI systems that perform the review tasks at the heart of litigation discovery: determining which documents in a collected corpus are responsive to the requests, which are privileged (and why — the privilege log being its own deliverable), which relate to which issues in the case, and which are the hot documents that shape strategy. The corpus scale is what makes discovery the legal industry's costliest document problem — millions of emails, attachments, chats, and files per significant matter — and what made it the earliest legal domain to accept machine assistance, with technology-assisted review (TAR) court-endorsed years before the LLM era.
The agentic generation upgrades TAR's statistical classifiers with reading comprehension. Where classic predictive coding learned responsiveness from seed sets as a text-classification problem, LLM-based review agents apply the review protocol as instructions — assessing each document against the actual request language, explaining the responsiveness call, spotting privilege markers that live in meaning rather than keywords (legal advice sought or given, not just a lawyer's name in the header), and drafting the privilege log entries. The explanations matter doubly: they accelerate the human QC that defensibility requires, and they surface protocol ambiguities early, while they're cheap to fix.
Defensibility is the governing constraint, because discovery process is itself litigable. Sampling-based validation with disclosed statistics (recall estimates on responsiveness are the accepted currency), documented protocols and prompt/model versions, human review of privilege calls before production (the error asymmetry — a privileged document produced is a bell hard to unring — keeps humans on that line), and audit trails of every coding decision. Courts' acceptance has followed the same path as TAR's: not "is it perfect" but "is it measured, transparent, and better than the alternative" — a bar exhausted human review teams at scale demonstrably struggle to meet.
Before review comes the grind: collecting, extracting, deduplicating, and staging a million files for the reviewers.
The review room, industrialized — responsiveness, privilege, and the hot documents, at corpus scale.
Litigation is reasonably anticipated — now nothing relevant may be deleted, and the system must make that true.
Proof Perimeter runs document AI inside your own perimeter — with a provenance record on every field.
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